一、空间金字塔池化
SPP
# SPP结构,利用不同大小的池化核进行池化 5*5 9*9 13*13
# 先构建kernel_size=5, stride=1, padding=2的最大池化层
# 再构建kernel_size=9, stride=1, padding=4的最大池化层
# 再构建kernel_size=13, stride=1, padding=6的最大池化层
# 池化后堆叠
#---------------------------------------------------#
class SpatialPyramidPooling(nn.Module):
def __init__(self, pool_sizes=[5, 9, 13]):
super(SpatialPyramidPooling, self).__init__()
self.maxpools = nn.ModuleList([nn.MaxPool2d(kernel_size=pool_size, stride=1, padding=pool_size//2) for pool_size in pool_sizes])
def forward(self, x):
features = [maxpool(x) for maxpool in self.maxpools[::-1]]
features = torch.cat(features + [x], dim=1) # x指的是未经过最大池化的层
return features
SPPF
class SPPF(nn.Module):
# Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher
def __init__(self, c1, c2, k=5): # equivalent to SPP(k=(5, 9, 13))
super().__init__()
c_ = c1 // 2 # hidden channels
self.cv1 = Conv(c1, c_, 1, 1)
self.cv2 = Conv(c_ * 4, c2, 1, 1)
self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2)
def forward(self, x):
x = self.cv1(x)
with warnings.catch_warnings():
warnings.simplefilter('ignore') # suppress torch 1.9.0 max_pool2d() warning
y1 = self.m(x)
y2 = self.m(y1)
return self.cv2(torch.cat([x, y1, y2, self.m(y2)], 1))
SPPCSPC
class SPPCSPC(nn.Module):
# CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5, k=(5, 9, 13)):
super(SPPCSPC, self).__init__()
c_ = int(2 * c2 * e) # hidden channels
self.cv1 = Conv(c1, c_, 1, 1)
self.cv2 = Conv(c1, c_, 1, 1)
self.cv3 = Conv(c_, c_, 3, 1)
self.cv4 = Conv(c_, c_, 1, 1)
self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])
self.cv5 = Conv(4 * c_, c_, 1, 1)
self.cv6 = Conv(c_, c_, 3, 1)
self.cv7 = Conv(2 * c_, c2, 1, 1)
def forward(self, x):
x1 = self.cv4(self.cv3(self.cv1(x)))
y1 = self.cv6(self.cv5(torch.cat([x1] + [m(x1) for m in self.m], 1)))
y2 = self.cv2(x)
return self.cv7(torch.cat((y1, y2), dim=1))
使用方式
第一步 各个代码放入common.py中
第二步 找到yolo.py文件里的parse_model函数,将类名加入进去
第三步 修改配置文件
在我自己的数据集上跑了一下,发现 SPPCSPC的效果是最好的~~~
二、上采样方式
1. 最近邻插值(Nearest neighbor interpolation)
YOLOV5中默认使用的是最近邻插值‘nearest’
2. 双线性插值(Bi-Linear interpolation)
若要改为双线性插值只需在yaml文件中将nearest改为bilinear,然后在后面加上True即可
文章来源:https://www.toymoban.com/news/detail-524802.html
reference
空间金字塔池化改进 SPP / SPPF / ASPP / RFB / SPPCSPC_迪菲赫尔曼的博客-CSDN博客文章来源地址https://www.toymoban.com/news/detail-524802.html
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